Introduction to scRNA-seq - Part IV: Annotation
Maria Vlachonikolou
30/05/2023
Part 4.1: Rough annotation
Please see this part for how to apply the annotation to the clustered data set and how to create the plots. This part (4.1) should be included in the report.
Read packages and data
library(Seurat)## Attaching SeuratObjectlibrary(clustree)## Loading required package: ggraph## Loading required package: ggplot2library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)Clean environment
rm(list = ls())
gc()## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 3081136 164.6 5002700 267.2 NA 5002700 267.2
## Vcells 5280843 40.3 10146329 77.5 16384 8187290 62.5Load Data
ple5 <- readRDS("srt_03_ple5v2.rds")
ple5.markers<-readRDS("markers_res_0.5v2.rds")Plot Clusters
DimPlot(ple5, group.by="RNA_snn_res.0.5", label=TRUE)
Annotating cell types
Each cell will be assigned a broad cell type based on the gene expression profiles.
Assign broad cell names to glutamatergic cells
Our data come from the telencephalon of the salamander Pleurodeles waltl as published in Woych et al., 2022. To annotate the clusters information from the aforementioned publication, as well a marker gene database of the mouse brain (http://mousebrain.org/adolescent/celltypes.html) were used. It should be kept in mind though, that amphibians and mammals are very distantly related and there are significant differences when it comes to cell types and gene expression profiles.
Sort markers by p_val_adj for each cluster.
for (i in 0:18) {
current_cluster_markers <- ple5.markers %>%
dplyr::filter(cluster == i) %>%
dplyr::arrange(p_val_adj)
assign(paste0("cluster", i, "_markers"), current_cluster_markers)
}According to the original publication “Telencephalic GABAergic neurons express markers of the subpallium, such as Dlx5, Gad1, and Gad2.”
ple5 <- SetIdent(ple5, value="RNA_snn_res.0.5")
FeaturePlot(ple5, features=c("DLX5", "GAD1", "GAD2"), label=TRUE, pt.size = 0.1)
It appears that clusters 0, 4, 5, 6, 7, 10, 14 and 15 are inhibitory
GABAergic neurons.
In the publication SLC17A7 is the marker gene for telencephalic glutamatergic neurons (TEGLU).
FeaturePlot(ple5, features="SLC17A7", label=TRUE, pt.size = 0.1)
Clusters 2, 3, 8, 9, 11 and 18 are excitatory glutamatergic neurons.
Cluster 0
cluster0_markers[1:10,0]## data frame with 0 columns and 10 rowsSHISA8, TSHZ1 and SP9 are olfactory expressed genes according to mouse brain data base. GAD1/2 are expressed in cluster 0. Those cells are inhibitory and have GABA as their primary neurotransmitter (GABAergic).
Clusters 1
cluster1_markers[1:5,0]## data frame with 0 columns and 5 rowsMDK and GJA1 are expressed in glial cells.
In the original publication, SOX9 is a marker for ependymoglia cells (EG).
VlnPlot(ple5, features=c("SOX9"))
Thus cluster 1 is characterized as ependymoglia cells.
Cluster 2
cluster2_markers[1:5,0]## data frame with 0 columns and 5 rowsAccording to the database, HPCAL1 and ADCYAP1.2 are expressed in excitatory neurons in the telencephalon. This also agrees with the SLC17A7 expression in this cluster. Thus cluster 2 is classified as excitatory glutamatergic neurons of the telencephalon (TEGLUT).
VlnPlot(ple5, features=c("HPCAL1", "ADCYAP1"))
Cluster 3 According to the publication, the amygdala is localized near the telencephalon and some cells from it might have found their way into the samples taken. The amygdala is demarcated by the expression of Slc17a6 and Nr2f2 and the absence of Sox6.
VlnPlot(ple5, features=c("SLC17A6", "NR2F2", "SOX6"))
Cluster 3 appears to match with this description.
Cluster 4
cluster4_markers[1:10,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## ZIC2 1.265573e-224 2.629953 0.915 0.090 1.658281e-220 4 ZIC2
## PRDM16.1 5.119572e-220 1.842833 0.829 0.065 6.708176e-216 4 PRDM16
## SCN5A 1.288534e-203 1.825381 0.658 0.034 1.688366e-199 4 SCN5A
## ZIC5 1.806208e-185 1.384147 0.658 0.040 2.366674e-181 4 ZIC5
## ZIC4 1.544861e-172 1.537982 0.824 0.091 2.024232e-168 4 ZIC4
## ZIC3 2.368269e-157 1.232324 0.774 0.089 3.103143e-153 4 ZIC3
## PRDM12 1.359533e-153 1.060871 0.407 0.009 1.781396e-149 4 PRDM12
## ZIC1.2 2.773102e-125 2.162296 0.955 0.307 3.633596e-121 4 ZIC1
## TNNI2 3.776805e-122 1.013863 0.472 0.033 4.948748e-118 4 TNNI2
## NXPH1 6.652924e-100 1.252521 0.588 0.080 8.717327e-96 4 NXPH1According to the database, PRDM16 and NXPH1 are expressed in inhibitory GABAergic neurons in the telencephalon. This also agrees with the GAD1/2 expression in this cluster. Thus cluster 4 is classified as TEGABA cells.
Cluster 5
cluster5_markers[1:5,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## SHISA8.1 1.118125e-159 2.0463273 0.923 0.124 1.465079e-155 5 SHISA8
## SALL3 6.613116e-132 1.6191852 0.740 0.089 8.665165e-128 5 SALL3
## FRMD7 7.223920e-119 0.5162602 0.284 0.003 9.465503e-115 5 FRMD7
## IL17RB 9.994641e-119 0.6061951 0.308 0.006 1.309598e-114 5 IL17RB
## MEIS3.5 4.648662e-112 1.6514132 0.953 0.217 6.091142e-108 5 MEIS3According to the database, SHISA8, SALL3 and FRMD7 are expressed in the olfactory bulb. Similarly to cluster 0, they also express GAD1/2. Thus cluster 5 is classified as OBGABA cells.
Clusters 6, 7, 15
Based mainly on the expression of DLX5, GAD1 and GAD2, these clusters are classified as TEGABA.
Cluster 8
cluster8_markers[1:5,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## LHX9 2.601509e-198 1.3616114 0.728 0.037 3.408757e-194 8 LHX9
## PROX1 1.251825e-141 1.0655459 0.550 0.029 1.640266e-137 8 PROX1
## Trhr2.1 2.648372e-128 1.5108549 0.781 0.094 3.470162e-124 8 Trhr2
## cbln4 7.030047e-128 1.4724907 0.735 0.083 9.211471e-124 8 cbln4
## MGAT4C 9.213471e-122 0.7366753 0.682 0.072 1.207241e-117 8 MGAT4CAccording to the database, PROX1 and MGAT4C are expressed in excitatory neurons in the telencephalon. This also agrees with the SLC17A7 expression in this cluster. Thus cluster 8 is classified as excitatory glutamatergic neurons of the telencephalon (TEGLUT).
Cluster 9
cluster9_markers[1:5,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## PHF2.5 5.874522e-30 -0.7291210 0.122 0.814 7.697387e-26 9 PHF2
## SETBP1.3 9.812078e-30 -0.7914181 0.157 0.858 1.285677e-25 9 SETBP1
## SMARCE1 9.994600e-30 -0.2648791 0.122 0.826 1.309592e-25 9 SMARCE1
## M6PR.1 1.395972e-29 -0.6659022 0.043 0.671 1.829143e-25 9 M6PR
## EIF4G1.1 1.582265e-29 -0.5498840 0.087 0.758 2.073241e-25 9 EIF4G1This cluster is mostly defined by the abscence of certain gene transcripts. Based on SLC17A7 expression in this cluster, it is classified as excitatory glutamatergic neurons of the telencephalon (TEGLUT).
Cluster 10
cluster10_markers[1:5,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## PDYN 2.229147e-211 3.1016152 0.654 0.017 2.920851e-207 10 PDYN
## TAC1 1.157410e-168 4.0041794 0.776 0.047 1.516554e-164 10 TAC1
## ADRA2B 5.616633e-163 0.7611294 0.458 0.008 7.359474e-159 10 ADRA2B
## ZNF503.1 1.065105e-116 1.0059643 0.794 0.082 1.395606e-112 10 ZNF503
## RASD2.1 9.478990e-113 1.6575809 0.813 0.095 1.242032e-108 10 RASD2According to the database, PDYN, TAC1 and RASD2.1 are expressed in inhibitory GABAergic neurons in the telencephalon. This also agrees with the GAD1/2 expression in this cluster. Thus cluster 10 is classified as TEGABA cells.
Cluster 11
cluster11_markers[1:10,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## KCNK3 1.818557e-170 1.1124605 0.758 0.040 2.382855e-166 11 KCNK3
## ARSJ 3.644082e-154 0.7474137 0.667 0.031 4.774841e-150 11 ARSJ
## AMIGO3 4.483239e-148 0.7425097 0.576 0.022 5.874388e-144 11 AMIGO3
## C11orf52.1 3.378173e-130 0.9203419 0.717 0.051 4.426420e-126 11 C11orf52
## NPTX1 5.421971e-127 4.0275194 1.000 0.162 7.104409e-123 11 NPTX1
## TESC.1 2.312956e-123 1.4817564 0.939 0.113 3.030666e-119 11 TESC
## DRAXIN.1 2.254627e-108 1.6689874 0.879 0.116 2.954238e-104 11 DRAXIN
## KANK4.1 8.209235e-102 1.3416315 0.899 0.123 1.075656e-97 11 KANK4
## TBR1.1 4.373672e-100 1.1838994 0.919 0.124 5.730823e-96 11 TBR1
## GPR158.1 4.648132e-97 1.5164614 0.929 0.171 6.090447e-93 11 GPR158According to the database, ARSJ, NPTX1, TESC.1 and DRAXIN.1 are expressed in excitatory neurons in the telencephalon. This also agrees with the SLC17A7 expression in this cluster. Thus cluster 11 is classified as excitatory glutamatergic neurons of the telencephalon (TEGLUT).
It is interesting that KCNK3, KANK4.1 and AMIGO3 are also associated with the neural crest in mice…
Cluster 12
cluster12_markers[1:5,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## C1QB 0 4.807704 0.750 0.003 0 12 C1QB
## CSF1R 0 4.228635 0.703 0.003 0 12 CSF1R
## C1QA 0 4.102269 0.719 0.003 0 12 C1QA
## TREM2 0 4.066687 0.781 0.002 0 12 TREM2
## ARPC1B 0 3.918109 0.969 0.013 0 12 ARPC1BVlnPlot(ple5, features="C1QB")
C1QB is a marker gene for microglia (MG) according to the original
publication. Indeed, cluster 12 has by far the highest expression of
this marker gene and is defined primarly by it.
Cluster 13
cluster13_markers[1:10,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## AGT 0.000000e+00 5.415024 0.982 0.011 0.000000e+00 13 AGT
## OLFML3 0.000000e+00 4.814740 0.964 0.010 0.000000e+00 13 OLFML3
## GJB6 0.000000e+00 4.220532 0.875 0.005 0.000000e+00 13 GJB6
## SLC38A4 0.000000e+00 3.745592 0.821 0.003 0.000000e+00 13 SLC38A4
## ATP8B3 0.000000e+00 3.520824 0.839 0.007 0.000000e+00 13 ATP8B3
## LUM 0.000000e+00 3.329207 0.786 0.005 0.000000e+00 13 LUM
## SLC6A13 0.000000e+00 2.425458 0.714 0.002 0.000000e+00 13 SLC6A13
## COL20A1 0.000000e+00 2.374799 0.768 0.001 0.000000e+00 13 COL20A1
## slc35c2 0.000000e+00 1.628434 0.661 0.001 0.000000e+00 13 slc35c2
## SLC13A2 4.559943e-299 2.141380 0.625 0.001 5.974894e-295 13 SLC13A2GJB6 and SLC6A13 are expressed in vascular cells (VC) according to the database.
In the publication COL1A2 is a marker gene for VCs.
VlnPlot(ple5, features=c("GJB6", "SLC6A13", "COL1A2"))
The above data agree with the hypothesis that Cluster 13 consists of
vascular cells.
Cluster 14
cluster14_markers[1:5,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## LHX6 1.871491e-165 1.0051091 0.821 0.020 2.452215e-161 14 LHX6
## BMP3 3.861901e-116 1.6591241 0.641 0.019 5.060248e-112 14 BMP3
## SHCBP1 2.087033e-81 0.4253901 0.410 0.010 2.734639e-77 14 SHCBP1
## MAF 1.015776e-75 0.6168007 0.487 0.018 1.330971e-71 14 MAF
## PCSK6.2 7.117844e-70 2.0468015 0.974 0.103 9.326511e-66 14 PCSK6According to the database, LHX6 and MAF are expressed in inhibitory GABAergic neurons in the telencephalon. This also agrees with the GAD1/2 expression in this cluster. Thus cluster 14 is classified as TEGABA cells.
Cluster 16
cluster16_markers[1:5,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## NKX2-2 0.000000e+00 2.461787 0.943 0.004 0.000000e+00 16 NKX2-2
## UGT8 0.000000e+00 2.087565 0.943 0.007 0.000000e+00 16 UGT8
## GJB1 5.308897e-245 1.277924 0.686 0.004 6.956247e-241 16 GJB1
## BCAS1 1.882655e-243 3.245117 0.971 0.015 2.466843e-239 16 BCAS1
## SOX10 3.483498e-225 1.921152 0.943 0.016 4.564428e-221 16 SOX10According to the database, NKX2-2, UGT8, GJB1, BCAS1 and SOX10 are markers for Oligodentrocytes.
In the original publication, NINJ2 is a marker for Oligodendrocytes (Olig). PDGFRA, GFAP and SOX2 are markers for oligodendrocyte precursor cells (OPC).
VlnPlot(ple5, features=c("NINJ2", "PDGFRA", "GFAP"))
Check if Olig and OPC markers are expressed in the same cells.
FeaturePlot(ple5, features=c("NINJ2", "GFAP"), blend=TRUE, pt.size = 0.1)
As separate cells in cluster 16 express markers for both Oligodentrocytes and oligodendrocyte precursor cells, the cluster seems both to be a mix of the two.
Cluster 17
cluster17_markers[1:20,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## PLP1 0.000000e+00 3.5394091 1.000 0.007 0.000000e+00 17 PLP1
## FOXD3 0.000000e+00 2.6648292 0.966 0.000 0.000000e+00 17 FOXD3
## EMP1 0.000000e+00 2.4445248 0.931 0.006 0.000000e+00 17 EMP1
## COL23A1 1.103640e-242 4.4754481 1.000 0.014 1.446100e-238 17 COL23A1
## CDH19.1 9.588781e-235 2.3768486 0.897 0.011 1.256418e-230 17 CDH19
## SOX10.1 5.556994e-215 2.5353671 1.000 0.017 7.281329e-211 17 SOX10
## MMP21 1.769177e-211 0.8379563 0.448 0.000 2.318152e-207 17 MMP21
## Hmu 5.953708e-193 0.8252596 0.379 0.000 7.801144e-189 17 Hmu
## TGM2 2.781182e-189 2.1962025 0.690 0.007 3.644182e-185 17 TGM2
## BCAS1.1 2.073947e-181 2.7793688 0.931 0.018 2.717493e-177 17 BCAS1
## SMOC1 1.162292e-162 1.3891680 0.655 0.009 1.522952e-158 17 SMOC1
## MATN2.1 3.111484e-160 1.9157914 0.862 0.019 4.076978e-156 17 MATN2
## SP5 6.211295e-158 1.4750334 0.621 0.008 8.138660e-154 17 SP5
## PRSS56 5.846051e-156 1.5930428 0.483 0.003 7.660081e-152 17 PRSS56
## ABCA9.1 7.317769e-152 4.6154831 1.000 0.031 9.588472e-148 17 ABCA9
## CNDP1 7.948127e-149 1.1587142 0.483 0.004 1.041443e-144 17 CNDP1
## MMP25 2.951980e-146 0.6650071 0.345 0.001 3.867979e-142 17 MMP25
## LAMB1.1 3.386741e-142 1.8714983 0.828 0.020 4.437646e-138 17 LAMB1
## MUSK 1.055070e-140 0.6984520 0.276 0.000 1.382458e-136 17 MUSK
## eva1c 5.648753e-140 1.2631751 0.517 0.006 7.401561e-136 17 eva1cPLP1 is a marker for glial cells according to the database.
In the publication PRSS56 is the marker gene for olfactory ensheathing cells (OEC), a type of microglia cells.
VlnPlot(ple5, features=c("PRSS56", "PLP1"))
Indeed, PRSS56 is expressed exclusively in cluster 17, marking it as
OEC.
Cluster 18
cluster18_markers[1:10,]## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## TBX21 0.000000e+00 2.1402989 0.80 0.003 0.000000e+00 18 TBX21
## EOMES 9.073989e-235 2.3627015 0.88 0.009 1.188965e-230 18 EOMES
## TMEM200B 2.568932e-209 1.2979851 0.68 0.005 3.366072e-205 18 TMEM200B
## CDHR1 1.011829e-195 3.1622721 0.96 0.016 1.325799e-191 18 CDHR1
## SYTL4 3.586036e-163 1.1692959 0.68 0.008 4.698783e-159 18 SYTL4
## TFAP2E 3.596488e-163 0.8224352 0.32 0.000 4.712479e-159 18 TFAP2E
## NDNF.1 1.733739e-79 1.9400447 0.68 0.024 2.271718e-75 18 NDNF
## RSPO3 5.957526e-74 0.9755796 0.48 0.012 7.806146e-70 18 RSPO3
## TFAP2C 1.103026e-63 0.7155998 0.44 0.012 1.445295e-59 18 TFAP2C
## RASSF2.1 3.803375e-61 1.0992209 0.72 0.039 4.983563e-57 18 RASSF2TBX21 is a marker gene for neuroblasts according to the mouse database. In the publication, immature neurons are characterized by the expression of SOX4.
VlnPlot(ple5, features=c("SOX4", "TBX21"))
SOX4 has the highest expression in cluster 18.
Thus, cells in cluster 18 are immature neurons (ImN).
ple5 <- SetIdent(ple5,value = "RNA_snn_res.0.5")
ple5 <- RenameIdents(object = ple5,
"0" = "OBGABA",
"1" = "EG",
"2" = "TEGLUT",
"3" = "nonTE.GLUT",
"4" = "TEGABA",
"5" = "OBGABA",
"6" = "TEGABA",
"7" = "TEGABA",
"8" = "TEGLUT",
"9" = "TEGLUT",
"10" = "TEGABA",
"11" = "TEGLUT",
"12" = "MG",
"13" = "VC",
"14" = "TEGABA",
"15" = "TEGABA",
"16" = "Olig/OPC",
"17" = "OEC",
"18" = "ImN"
)
DimPlot(ple5, label=TRUE)
Create metadata column for broad cell types
Now we will save the information about broad cell type as a column in the metadata.
ple5$broad.cell.types <- as.character(Idents(ple5))head(ple5[[]])## orig.ident nCount_RNA nFeature_RNA percent_mito
## TCAGCCTGTTTACGTG Pleurodeles 29999 5725 0.6266876
## CAACAGTGTTAGAAGT Pleurodeles 29764 5733 1.8411504
## CATGCGGGTCCTACAA Pleurodeles 29703 5834 1.1210989
## AAACCCATCCTTCTTC Pleurodeles 29647 5733 0.4081357
## CCTCACAAGATGGGCT Pleurodeles 29645 5801 1.0895598
## GAAGGGTAGATAGCTA Pleurodeles 29640 5734 0.3609987
## RNA_snn_res.0.1 RNA_snn_res.0.5 RNA_snn_res.0.7 RNA_snn_res.1
## TCAGCCTGTTTACGTG 5 10 10 11
## CAACAGTGTTAGAAGT 3 8 8 7
## CATGCGGGTCCTACAA 3 2 1 0
## AAACCCATCCTTCTTC 3 2 1 0
## CCTCACAAGATGGGCT 0 6 5 3
## GAAGGGTAGATAGCTA 3 8 8 7
## RNA_snn_res.1.5 RNA_snn_res.2 RNA_snn_res.3 seurat_clusters
## TCAGCCTGTTTACGTG 10 9 7 7
## CAACAGTGTTAGAAGT 4 2 17 17
## CATGCGGGTCCTACAA 6 18 19 19
## AAACCCATCCTTCTTC 13 11 10 10
## CCTCACAAGATGGGCT 1 21 20 20
## GAAGGGTAGATAGCTA 4 2 11 11
## broad.cell.types
## TCAGCCTGTTTACGTG TEGABA
## CAACAGTGTTAGAAGT TEGLUT
## CATGCGGGTCCTACAA TEGLUT
## AAACCCATCCTTCTTC TEGLUT
## CCTCACAAGATGGGCT TEGABA
## GAAGGGTAGATAGCTA TEGLUTVisualize the number of cell counts per cell type
data <- as.data.frame(table(ple5$broad.cell.types))
colnames(data) <- c("cell.type","count")
ncells <- ggplot(data, aes(x = cell.type, y = count, fill = cell.type)) +
geom_col() +
theme_classic() +
geom_text(aes(label = count),
position=position_dodge(width=0.9),
vjust=-0.25) +
ggtitle("Cells per cell type") +
theme(legend.position = "none") +
theme(axis.text.x = element_text(angle = 45, hjust=1))
ncells
TEGABA and TEGLUT have the largest number of counts (cells), while
Immature Neurons and Olfactory Ensheathing Cells are the fewest.
Make a DotPlot to visualize gene expression across clusters
Let’s make a dotplot as another way to compare expression levels between clusters. Let’s try to recreate Fig.1D from the Woych et al (2022) paper.
Major_Features <- c("SNAP25","SYT1","RBFOX3","FOXG1","SLC17A7","GAD1",
"SLC17A6","SOX4","SOX9","SOX2","GFAP","PDGFRA",
"NINJ2","COL1A2","PRSS56","C1QB","LCP1")
ple5 <- SetIdent(ple5,value = "broad.cell.types")
DotPlot(ple5, features=Major_Features)
Now let’s reorder the clusters so it looks even more like their figure.
head(Idents(ple5))## TCAGCCTGTTTACGTG CAACAGTGTTAGAAGT CATGCGGGTCCTACAA AAACCCATCCTTCTTC
## TEGABA TEGLUT TEGLUT TEGLUT
## CCTCACAAGATGGGCT GAAGGGTAGATAGCTA
## TEGABA TEGLUT
## Levels: TEGABA TEGLUT ImN nonTE.GLUT OBGABA Olig/OPC EG VC MG OECIdents(ple5) <- factor(Idents(ple5),
levels=c("MG","OEC","VC","Olig/OPC",
"EG","ImN",
"OBGABA", "nonTE.GLUT", "TEGABA","TEGLUT"))DotPlot(ple5, features=Major_Features) +
theme(axis.text.x = element_text(angle = 45, vjust=0.9, hjust=0.9))
The graph looks very similar to the original publication. SNAP25, SYT1,
RBFOX3 and FOXG1, although not used for cluster annotation, agree well
with the published figure.
We can alternatively plot gene expression using heatmaps. Here each column represents a cell and each row is a gene.
DoHeatmap(subset(ple5, downsample = 50), features = Major_Features, size = 3)## Warning in DoHeatmap(subset(ple5, downsample = 50), features = Major_Features,
## : The following features were omitted as they were not found in the scale.data
## slot for the RNA assay: FOXG1, RBFOX3
Save the Seurat object with the broad annotation for day 5
saveRDS(ple5, "srt_04_ple5v2.rds")sessionInfo()## R version 4.3.0 (2023-04-21)
## Platform: x86_64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.6.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Berlin
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dplyr_1.1.2 clustree_0.5.0 ggraph_2.1.0 ggplot2_3.4.2
## [5] SeuratObject_4.1.3 Seurat_4.3.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.14 jsonlite_1.8.4
## [4] magrittr_2.0.3 spatstat.utils_3.0-3 farver_2.1.1
## [7] rmarkdown_2.21 vctrs_0.6.2 ROCR_1.0-11
## [10] spatstat.explore_3.1-0 htmltools_0.5.5 sass_0.4.6
## [13] sctransform_0.3.5 parallelly_1.35.0 KernSmooth_2.23-21
## [16] bslib_0.4.2 htmlwidgets_1.6.2 ica_1.0-3
## [19] plyr_1.8.8 plotly_4.10.1 zoo_1.8-12
## [22] cachem_1.0.8 igraph_1.4.2 mime_0.12
## [25] lifecycle_1.0.3 pkgconfig_2.0.3 Matrix_1.5-4
## [28] R6_2.5.1 fastmap_1.1.1 fitdistrplus_1.1-11
## [31] future_1.32.0 shiny_1.7.4 digest_0.6.31
## [34] colorspace_2.1-0 patchwork_1.1.2 tensor_1.5
## [37] irlba_2.3.5.1 labeling_0.4.2 progressr_0.13.0
## [40] fansi_1.0.4 spatstat.sparse_3.0-1 httr_1.4.6
## [43] polyclip_1.10-4 abind_1.4-5 compiler_4.3.0
## [46] withr_2.5.0 viridis_0.6.3 highr_0.10
## [49] ggforce_0.4.1 MASS_7.3-60 tools_4.3.0
## [52] lmtest_0.9-40 httpuv_1.6.10 future.apply_1.10.0
## [55] goftest_1.2-3 glue_1.6.2 nlme_3.1-162
## [58] promises_1.2.0.1 grid_4.3.0 Rtsne_0.16
## [61] cluster_2.1.4 reshape2_1.4.4 generics_0.1.3
## [64] gtable_0.3.3 spatstat.data_3.0-1 tidyr_1.3.0
## [67] data.table_1.14.8 tidygraph_1.2.3 sp_1.6-0
## [70] utf8_1.2.3 spatstat.geom_3.2-1 RcppAnnoy_0.0.20
## [73] ggrepel_0.9.3 RANN_2.6.1 pillar_1.9.0
## [76] stringr_1.5.0 later_1.3.1 splines_4.3.0
## [79] tweenr_2.0.2 lattice_0.21-8 survival_3.5-5
## [82] deldir_1.0-6 tidyselect_1.2.0 miniUI_0.1.1.1
## [85] pbapply_1.7-0 knitr_1.42 gridExtra_2.3
## [88] scattermore_1.0 xfun_0.39 graphlayouts_1.0.0
## [91] matrixStats_0.63.0 stringi_1.7.12 lazyeval_0.2.2
## [94] yaml_2.3.7 evaluate_0.21 codetools_0.2-19
## [97] tibble_3.2.1 cli_3.6.1 uwot_0.1.14
## [100] xtable_1.8-4 reticulate_1.28 munsell_0.5.0
## [103] jquerylib_0.1.4 Rcpp_1.0.10 globals_0.16.2
## [106] spatstat.random_3.1-4 png_0.1-8 parallel_4.3.0
## [109] ellipsis_0.3.2 listenv_0.9.0 viridisLite_0.4.2
## [112] scales_1.2.1 ggridges_0.5.4 leiden_0.4.3
## [115] purrr_1.0.1 rlang_1.1.1 cowplot_1.1.1